Semantic Web Component Mapping for Automated Page Integration

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Solution Overview

Problem

Existing methods for integrating web-based components into webpages require substantial human labor and manual identification of placement, lacking automation and personalization, and struggle with dynamic content analysis to meet pre-determined requirements.

Innovation Solution

Automatically determine the semantic meaning of visually separated components in digital content using machine learning and heuristics, allowing for automated integration and adherence to requirements without user intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual identification and integration of web-based components is used, then integration accuracy and control are improved, but time consumption and labor resources increase significantly

Engineering Contradiction:

Improveintegration accuracyVSAvoidtime consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system enables automatic self-identification of component semantic meanings and self-integration into webpages without manual intervention. The machine learning model analyzes digital content, determines semantic meanings of components, and automatically integrates web-based components based on determined meanings, allowing the system to serve itself rather than requiring human operators for each integration task

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes of component identification and integration with an automated machine learning-based system. The machine learning model processes digital content, extracts features, determines semantic meanings, and triggers automatic integration actions, substituting human cognitive and manual operations with computational processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If automated integration is implemented, then time consumption is reduced, but the ability to meet pre-determined requirements and ensure compliance may worsen

Engineering Contradiction:

Improveintegration timeVSAvoidrequirement compliance

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms where the machine learning model continuously analyzes digital content, determines semantic meanings of components, and adjusts integration decisions based on analyzed features and pre-determined requirements. This feedback loop ensures that automated integration actions comply with requirements by constantly referencing and adapting to specified criteria

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of digital content before integration, using machine learning to pre-determine semantic meanings of components and pre-identify appropriate integration locations. This preliminary action allows the system to prepare integration strategies in advance that are guaranteed to meet pre-determined requirements, rather than reacting after requirements are violated

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If manual component analysis is used, then semantic meaning accuracy is improved, but productivity and scalability are reduced

Engineering Contradiction:

Improvesemantic meaning accuracyVSAvoidintegration productivity

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual semantic analysis with machine learning models that automatically determine component semantic meanings by analyzing extracted features from digital content. This substitution enables high-accuracy semantic meaning determination at scale, as the machine learning system can process multiple components simultaneously without the productivity limitations of manual analysis

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning model serves multiple functions simultaneously: it analyzes digital content, extracts features, determines semantic meanings of components, and identifies integration locations. This multi-functionality consolidates what would otherwise require multiple separate manual processes into a single automated system that maintains accuracy while dramatically improving productivity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12481715B2Semantic meaning association to components of digital content

Publication Date: 2025.11.25 TABOOLA COM LTD

AI summary

A method, system, apparatus and product for semantic meaning association to components of digital content. The method comprising obtaining a digital content, which comprises multiple visually separated components. The method comprises analyzing at least a portion of the digital content to extract features associated with a component and automatically determining, based on the extracted features, a semantic meaning of the component. The automatic determination is performed without relying on manually inputted hints in the digital content. The method further comprises automatically and without user intervention, performing an action associated with the digital content, wherein the action is determined based on the semantic meaning.